Report Assessment Ch2 Technology Visions

Data Management Report

Author

Affiliation

Rainer M. Krug

Senckenberg

Abstract

A short description what this is about. This is not a tracditional abstract, but rather something else …

Working Title

IPBES_TCA_Ch2_technology

Code repo

Github - private

Build No: 64

%The BuidNo is automatically increased by one each time the report is rendered. It is used to indicate different renderings when the version stays the same%.

Introduction

All searches are done on all works in OpenAlex. The search in the TCA Corpus is not possibly at the moment, but we are working on it.

The following steps will be done in documented in this report:

• Do asearch with vision_st, technology_st, and vision_st AND technology_st and determine the number of hits
• Identify sub-fields (in topics) of hits
• Check filtering using sub-fields
• Should the complete vision_st AND technology_st be downloaded, or only a random subsets? Suggestiopn: 10 sets of 100 random works?

Step 1: Determination of numbers

The search terms are based on the shared google doc. They are cleaned up for the usage in OpenAlex.

Vision

The search terms is vision Open Alex search.

#|

vision_count <- openalexR::oa_fetch(
    title_and_abstract.search = vision_st,
    count_only = TRUE,
    output = "list",
    verbose = TRUE
)$count

Technology

The search terms is technology Open Alex search.

#|

technology_count <- openalexR::oa_fetch(
    title_and_abstract.search = compact(technology_st),
    count_only = TRUE,
    output = "list",
    verbose = TRUE
)$count

Vision AND technology

Open Alex search.

The search term is vision AND technology

Count

#|

vision_technology_count <-
    openalexR::oa_fetch(
        title_and_abstract.search = compact(paste0("(", vision_st, ") AND (", technology_st, ")")),
        output = "list",
        count_only = TRUE,
        verbose = TRUE
    )$count

Count Subfields

#|

vision_technology_subfields <- openalexR::oa_query(
    title_and_abstract.search = compact(paste0("(", vision_st, ") AND (", technology_st, ")")),
    group_by = "primary_topic.subfield.id",
    verbose = TRUE
) |>
    openalexR::oa_request() |>
    dplyr::bind_rows() |>
    dplyr::arrange(key)

## clean up missing or wrong vision_technology_subfields$key_display_name
need_cleaning <- is.na(vision_technology_subfields$key_display_name) |
    !is.na(as.numeric(vision_technology_subfields$key_display_name))

Warning: NAs introduced by coercion

fine <- !need_cleaning

vision_technology_subfields <- vision_technology_subfields |>
    dplyr::filter(fine) |>
    dplyr::select(key, key_display_name) |>
    dplyr::distinct() |>
    merge(y = vision_technology_subfields[need_cleaning, -2], by = "key") |>
    dplyr::bind_rows(vision_technology_subfields[fine, ]) |>
    dplyr::group_by(key, key_display_name) |>
    dplyr::summarize(count = sum(count))

Download technology AND vision Corpus

The corpus download will be stored in data/pages and the arrow database in data/corpus.

This is not on github!

The corpus can be read by running get_corpus() which o[pens the database so that then it can be fed into a dplyr pipeline. After most dplyr functions, the actual data needs to be collected via collect().

Only then is the actual data read!

Needs to be enabled by setting eval: true in the code block below.

Download TCA Corpus

#|

tic()
pages_dir <- file.path(".", "data", "pages")

dir.create(
    path = pages_dir,
    showWarnings = FALSE,
    recursive = TRUE
)

years <- oa_fetch(
    title_and_abstract.search = compact(paste0("(", vision_st, ") AND (", technology_st, ")")),
    group_by = "publication_year",
    paging = "cursor",
    verbose = FALSE
)$key

#######
#######
processed <- list.dirs(
    path = pages_dir,
    full.names = FALSE,
    recursive = FALSE
) |>
    gsub(
        pattern = paste0("^pages_publication_year=", ""),
        replacement = ""
    )

interrupted <- list.files(
    path = pages_dir,
    pattern = "^next_page.rds",
    full.names = TRUE,
    recursive = TRUE
) |>
    gsub(
        pattern = paste0("^", pages_dir, "/pages_publication_year=", ""),
        replacement = ""
    ) |>
    gsub(
        pattern = "/next_page.rds$",
        replacement = ""
    )

completed <- processed[!(processed %in% interrupted)]

years <- years[!(years %in% completed)]
#######
#######

pbmcapply::pbmclapply(
    sample(years),
    function(y) {
        message("\nGetting data for year ", y, " ...")
        output_path <- file.path(pages_dir, paste0("pages_publication_year=", y))

        dir.create(
            path = output_path,
            showWarnings = FALSE,
            recursive = TRUE
        )

        data <- oa_query(
            title_and_abstract.search = compact(paste0("(", vision_st, ") AND (", technology_st, ")")),
            publication_year = y,
            options = list(
                select = c("id", "doi", "authorships", "publication_year", "display_name", "abstract_inverted_index", "topics")
            ),
            verbose = FALSE
        ) |>
            IPBES.R::oa_request_IPBES(
                count_only = FALSE,
                output_path = output_path,
                verbose = TRUE
            )
    },
    mc.cores = 1,
    mc.preschedule = FALSE
)

toc()

Convert TCA Corpus to Arrow

The fields author and topics are serialized in the arrow database and need to be unserialized by using unserialize_arrow() on a dataset containing the two columns.

tic()

pages_dir <- file.path(".", "data", "pages")
arrow_dir <- file.path(".", "data", "corpus")

years <- list.dirs(
    path = pages_dir,
    full.names = TRUE,
    recursive = FALSE
)

years_done <- list.dirs(
    path = arrow_dir,
    full.names = TRUE,
    recursive = FALSE
)

years <- years[
    !(
        gsub(
            x = years,
            pattern = paste0("^", pages_dir, "/pages_publication_year="),
            replacement = ""
        ) %in% gsub(
            x = years_done,
            pattern = paste0("^", arrow_dir, "/publication_year="),
            replacement = ""
        )
    )
]

pbapply::pblapply(
    years,
    function(year) {
        message("\n     Processing year ", year, " ...\n")
        pages <- list.files(
            path = year,
            pattern = "^page_",
            full.names = TRUE,
            recursive = TRUE
        )
        invisible(
            pbmcapply::pbmclapply(
                pages,
                function(page) {
                    data <- readRDS(file.path(page))$results |>
                        openalexR::works2df(verbose = FALSE)
                    data$author_abbr <- IPBES.R::abbreviate_authors(data)
                    data <- serialize_arrow(data)

                    data$page <- page |>
                        basename() |>
                        gsub(pattern = "^page_", replacement = "") |>
                        gsub(pattern = ".rds$", replacement = "")

                    arrow::write_dataset(
                        data,
                        path = arrow_dir,
                        partitioning = c("publication_year", "page"),
                        format = "parquet",
                        existing_data_behavior = "overwrite"
                    )
                },
                mc.cores = 6 # params$mc.cores
            )
        )
    }
)
toc()

Filter Corpus with TCA Corpus

ids_technology <- read_corpus(file.path("data", "corpus")) |>
    dplyr::select(id) |>
    collect() |>
    unlist()

Warning: Invalid metadata$r

ids_tca <- read_corpus(file.path("..", "IPBES_TCA_Corpus", "data", "corpus")) |>
    dplyr::select(id) |>
    collect() |>
    unlist()

Warning: Invalid metadata$r

ids_subs_tca <- ids_technology[ids_technology %in% ids_tca]

arrow_tca_dir <- file.path(".", "data", "corpus_tca")
arrow_dir <- file.path(".", "data", "corpus")


year_dirs <- list.dirs(
    path = arrow_dir,
    full.names = TRUE,
    recursive = FALSE
)

year_done <- list.dirs(
    path = arrow_tca_dir,
    full.names = TRUE,
    recursive = FALSE
)

year_dirs <- year_dirs[!(basename(year_dirs) %in% basename(year_done))]

years <- basename(year_dirs) |>
    gsub(
        pattern = "publication_year=",
        replacement = ""
    )
ys <- seq_len(length(year_dirs))


pbapply::pblapply(
    ys,
    function(y) {
        data <- read_corpus(year_dirs[[y]]) |>
            dplyr::collect() |>
            dplyr::filter(id %in% ids_subs_tca)
        if (nrow(data) > 0) {
            data |>
                dplyr::mutate(publication_year = as.integer(years[[y]])) |>
                arrow::write_dataset(
                    path = arrow_tca_dir,
                    partitioning = "publication_year",
                    format = "parquet",
                    existing_data_behavior = "overwrite"
                )
        }
    }
)

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toc()

Extract 50 random papers from technology AND vision in TCA Corpus

#|

fn <- file.path("data", "random_50_technology_in_tca.xlsx")

if (!file.exists(fn)) {
    set.seed(14)
    read_corpus("data/corpus_tca") |>
        dplyr::select(id, author_abbr, display_name, ab) |>
        dplyr::rename(abstract = ab, title = display_name) |>
        dplyr::collect() |>
        dplyr::slice_sample(n = 50) |>
        dplyr::mutate(
            abstract = substr(abstract, start = 1, stop = 5000)
        ) |>
        writexl::write_xlsx(path = fn)
}

Results

vision

Hits for search term vision: 105,605,001 hits in OpenAlex.

Individual terms cobmbined by OR:

#|

assess_search_term(readLines(file.path("input", "vision.txt"))) |>
    dplyr::arrange(desc(count)) |>
    dplyr::mutate(count = formatC(count, format = "f", big.mark = ",", digits = 0)) |>
    knitr::kable()

term	count
model	20,927,440
process	16,682,151
effect	13,632,994
approach	12,696,246
value	11,127,761
activity	9,857,218
performance	9,812,586
technique	9,165,469
influence	8,608,597
response	8,436,545
relationships	6,971,303
objective	6,739,843
solution	6,642,628
strategy	6,119,457
image	6,088,314
view	5,448,214
future	5,370,203
target	5,231,536
reaction	4,779,218
knowledge	4,741,397
project	4,299,961
project	4,299,961
policy	4,235,675
action	3,918,602
plan	3,912,838
operation	3,773,495
culture	3,633,033
perspective	3,389,608
task	2,801,091
effort	2,635,904
government	2,492,824
idea	2,255,473
opportunity	2,211,126
transmission	2,168,545
respect	2,071,852
perception	1,715,098
platform	1,640,040
existence	1,580,823
movement	1,514,491
scenarios	1,469,098
innovation	1,230,430
desire	1,137,800
vision	1,111,827
visioning	1,111,827
reality	977,987
story	950,186
conceptual	911,789
motivation	867,911
appearance	848,699
responsibilities	843,344
visualization	823,644
initiative	793,884
moment	787,457
hope	754,107
discourse	719,019
iteration	706,046
cooperation	682,796
mission	662,205
territory	460,720
intention	442,892
agenda	409,691
wish	404,894
dialogue	378,949
consultation	324,349
aspiration	300,008
fiction	293,234
spiritual	273,170
co-production	242,175
imagery	240,930
creativity	239,666
universe	230,428
dream	199,293
sight	191,279
imagination	169,465
inspiration	164,273
cosmology	159,668
harmony	120,342
coalition	110,398
self-determination	98,243
solidarity	92,470
fantasy	75,665
roadmap	67,724
worldview	66,307
reciprocity	59,863
ceremony	59,657
“collective action”	40,651
visionary	28,403
programm	26,415
foresight	23,229
“participatory process”	6,498
cosmovision	2,896
“deliberate process”	607
communit	449
cosmocentric	98
languague	83
arquetype	5

technology

Hits for search term technology: 14,937,469 hits in OpenAlex.

Individual terms cobmbined by OR:

#|

assess_search_term(readLines(file.path("input", "technology.txt"))) |>
    dplyr::arrange(desc(count)) |>
    dplyr::mutate(count = formatC(count, format = "f", big.mark = ",", digits = 0)) |>
    knitr::kable()

term	count
Technology	6,358,098
Software	2,467,986
Machine-to-Machine	2,131,976
Internet	1,245,794
“Social Media”	1,059,530
Virtualization	1,033,802
Robotics	774,867
“Machine Learning”	675,928
“Deep Learning”	394,634
“Artificial Intelligence”	325,704
“Renewable Energy”	255,935
“Biotechnology”	235,095
IOT	213,696
“Big Data”	191,660
“Internet of Things”	180,973
“Computer Vision”	129,572
“Virtual Reality”	124,486
“Cloud Computing”	121,643
E-commerce	118,827
Nanotechnology	100,913
5G	95,726
Blockchain	89,136
“Natural Language Processing”	79,992
“Augmented Reality”	67,666
“Speech Recognition”	66,188
“3D Printing”	65,530
“Smart Grid”	56,562
“Genetic Engineering”	41,896
“Genetic engineering”	41,896
“Autonomous Vehicle”	41,368
“Digital Transformation”	40,298
“Circular Economy”	37,977
Cybersecurity	35,691
“Clean Energy”	33,823
“Blockchain Technology”	32,238
“Data Science”	30,990
“Edge Computing”	30,247
“Cyber-Physical Systems”	27,882
“Smart Home”	26,349
“Quantum Computing”	25,652
“Digital Twin”	23,059
Cryptocurrency	22,193
“Space Technology”	17,503
Fintech	14,852
“Application Programming Interface”	13,424
“Mixed Reality”	11,753
“Facial Recognition”	9,962
“Wearable Technology”	8,145
Microservices	7,575
“Sustainable Technology”	7,070
“Digital Currency”	5,743
“Agile Development”	4,866
“Computational Technology”	3,668
DevOps	3,491
“Digital Wallet”	966
“Internet Safety”	923
“Internet Privacy”	742
“Digital Ethics”	479

vision AND technology in TCA Corpus

For the TCA Corpus, we do have

read_corpus(file.path("data", "corpus_tca")) |>
    dplyr::select(id) |>
    dplyr::collect() |>
    nrow()

Warning: Invalid metadata$r

[1] 643474

number of works.

Random Sample of 50 works

The file contains the id, author_abbr and abstract of the papers. Two samples were generated:

• works in the technology corpus AND in the TCA corpus which can be downloded here.

Subfields

The subfields are based on the main topic assigned to each work. There are other topics also assigned, but this one has been identified as the main topic by an algorythm. count is the number of works in the vision AND technology corpus which have been assigned to the subfield.

Please take a look at these subfields of the topics to identify the ones to be filtered out.

The easies would be to download the Excel file through the button and to mark the subfields to be filtered out.

IPBES.R::table_dt(vision_technology_subfields, fixedColumns = NULL, fn = "Vision Technology Subfields")